tickets <- read.csv(file = "WHD_Tickets_2018.csv")
Time <- tickets %>% select(Scheduled.Date)
Time$Date <- str_split_fixed(Time$Scheduled.Date, " ", n = 2)[,1]
Time$begin <- str_split_fixed(Time$Scheduled.Date, " - ", n = 2)[,1]
Time$end <- paste(Time$Date, str_split_fixed(Time$Scheduled.Date, " - ", n = 2)[,2], sep = " ")
Time$Date <- as.Date(Time$Date, "%m/%d/%y")
Time$begin <- as.POSIXct(strptime(Time$begin, "%m/%d/%Y %I:%M %p"))
Time$end <- as.POSIXct(strptime(Time$end, "%m/%d/%Y %I:%M %p"))
winter <- Time %>% filter(between(Date, as.Date("2018-01-03"), as.Date("2018-03-14"))) %>% arrange(Date)
spring <- Time %>% filter(between(Date, as.Date("2018-03-26"), as.Date("2018-06-04"))) %>% arrange(Date)
fall <- Time %>% filter(between(Date, as.Date("2018-09-10"), as.Date("2018-11-19"))) %>% arrange(Date)
winter$term <- "Winter"
spring$term <- "Spring"
fall$term <- "Fall"
winter$Week <- floor(difftime(winter$Date, "2018-01-02", units = "weeks")) + 1
spring$Week <- floor(difftime(spring$Date, "2018-03-25", units = "weeks")) + 1
fall$Week <- floor(difftime(fall$Date, "2018-09-09", units = "weeks")) + 1
data.2018 <- rbind(winter, spring, fall)
data.2018$Week <- as.factor(data.2018$Week)
data.2018$dayofweek <- wday(data.2018$Date, label=TRUE)
data.2018 <- transform(data.2018, term=factor(term,levels=c("Fall","Winter","Spring")))
ggplotly(ggplot(data.2018) +
geom_bar(aes(Week)) +
facet_wrap(~term) +
coord_flip())
ggplot(data.2018) +
geom_bar(aes(dayofweek))
